Enhancement as Augmentation: Improving Detection in Highly Degraded Underwater Images Through Mixed-Domain Training

Document Type

Conference Proceeding

Publication Date

1-1-2026

Abstract

Underwater object detection is challenged by visibility degradation caused by absorption, scattering, and turbidity. Although underwater image enhancement (UIE) is often used as a preprocessing step, prior work shows that enhancement can distort appearance and reduce detector performance. We revisit UIE from a data augmentation perspective and propose a mixed-domain training framework in which original images are paired with enhanced variants generated by four state-of-the-art UIE models: ACDC, AutoEnhancer, TUDA, and Semi-UIR. This design isolates the effect of enhancement-induced domain shifts while keeping the detector architecture and inference pipeline unchanged. Experiments on a curated subset of the USGS round goby dataset reveal that perceptual enhancement quality does not predict detection effectiveness: the enhancer with the lowest UIQM, UCIQE, and CCF scores yields the strongest mAP improvement, whereas the highest-scoring enhancers produce the weakest detection results due to overenhancement. We benchmarked the evaluation metrics across the original, enhanced, and mixed-domain datasets, and observed that Mixed-domain training compensates for the effects of enhancement and consistently improves mAP@50, F1-score, and detection accuracy, demonstrating that enhancementas-augmentation is an effective and lightweight strategy for improving the robustness of underwater object detection.

Publication Title

Proceedings 2026 IEEE Cvf Winter Conference on Applications of Computer Vision Workshops Wacvw 2026

ISBN

[9798331591496]

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